results/orthofinder/output/Orthogroup_Sequences/OG0000000.fa
results/orthofinder/output/Orthogroup_Sequences/OG0000001.fa
results/orthofinder/output/Orthogroup_Sequences/OG0000002.fa
results/orthofinder/output/Orthogroup_Sequences/OG0000003.fa
results/orthofinder/output/Orthogroup_Sequences/OG0000004.fa
results/orthofinder/output/Orthogroup_Sequences/OG0000006.fa

results/orthofinder/orthosnap/OG0000005/OG0000005_orthosnap_0.fa

results/alignment/trimmed_protein/OG0000004.trimmed.protein.alignment.fa
results/alignment/trimmed_protein/OG0000000.trimmed.protein.alignment.fa

General Characteristics
=======================
6	Number of taxa
796	Alignment length
100	Parsimony informative sites
100	Variable sites
685	Constant sites

Character Frequencies
=====================
Y	176
W	52
V	338
T	242
S	200
R	217
Q	230
P	268
N	222
M	77
L	412
K	301
I	309
H	97
G	410
F	230
E	271
D	231
C	79
A	366
-	48

IQ-TREE 2.2.0.3 COVID-edition built Aug  2 2022

Input file name: results/supermatrix/supermatrix.protein.fa
Type of analysis: ModelFinder + tree reconstruction + ultrafast bootstrap (1000 replicates)
Random seed number: 413450

REFERENCES
----------

To cite IQ-TREE please use:

Bui Quang Minh, Heiko A. Schmidt, Olga Chernomor, Dominik Schrempf,
Michael D. Woodhams, Arndt von Haeseler, and Robert Lanfear (2020)
IQ-TREE 2: New models and efficient methods for phylogenetic inference
in the genomic era. Mol. Biol. Evol., in press.
https://doi.org/10.1093/molbev/msaa015

To cite ModelFinder please use: 

Subha Kalyaanamoorthy, Bui Quang Minh, Thomas KF Wong, Arndt von Haeseler,
and Lars S Jermiin (2017) ModelFinder: Fast model selection for
accurate phylogenetic estimates. Nature Methods, 14:587–589.
https://doi.org/10.1038/nmeth.4285

Since you used ultrafast bootstrap (UFBoot) please also cite: 

Diep Thi Hoang, Olga Chernomor, Arndt von Haeseler, Bui Quang Minh,
and Le Sy Vinh (2018) UFBoot2: Improving the ultrafast bootstrap
approximation. Mol. Biol. Evol., 35:518–522.
https://doi.org/10.1093/molbev/msx281

SEQUENCE ALIGNMENT
------------------

Input data: 6 sequences with 796 amino-acid sites
Number of constant sites: 553 (= 69.4724% of all sites)
Number of invariant (constant or ambiguous constant) sites: 553 (= 69.4724% of all sites)
Number of parsimony informative sites: 100
Number of distinct site patterns: 255

ModelFinder
-----------

Best-fit model according to BIC: cpREV+I+G4

List of models sorted by BIC scores: 

Model                  LogL         AIC      w-AIC        AICc     w-AICc         BIC      w-BIC
cpREV+I+G4        -4754.045    9530.090 +    0.864    9530.427 +    0.904    9581.566 +    0.563
cpREV+G4          -4757.639    9535.278 +   0.0645    9535.558 +   0.0695    9582.074 +    0.437
LG+I+G4           -4762.923    9547.846 -  0.00012    9548.183 - 0.000126    9599.322 - 7.85e-05
LG+G4             -4769.787    9559.575 - 3.42e-07    9559.855 - 3.68e-07    9606.371 - 2.31e-06
Q.pfam+I+G4       -4772.958    9567.915 - 5.28e-09    9568.252 - 5.53e-09    9619.391 - 3.44e-09
Q.pfam+G4         -4779.840    9579.680 - 1.47e-11    9579.960 - 1.59e-11    9626.476 - 9.96e-11
WAG+I+G4          -4778.199    9578.397 -  2.8e-11    9578.734 - 2.93e-11    9629.873 - 1.82e-11
WAG+G4            -4782.001    9584.002 -  1.7e-12    9584.283 - 1.83e-12    9630.798 - 1.15e-11
VT+G4             -4783.028    9586.055 - 6.08e-13    9586.335 - 6.54e-13    9632.851 - 4.11e-12
VT+I+G4           -4779.908    9581.816 - 5.06e-12    9582.153 -  5.3e-12    9633.291 -  3.3e-12
LG+I              -4785.119    9590.237 - 7.51e-14    9590.517 - 8.08e-14    9637.033 - 5.08e-13
Q.plant+I+G4      -4784.005    9590.009 - 8.41e-14    9590.346 - 8.81e-14    9641.485 - 5.49e-14
Q.yeast+I+G4      -4784.243    9590.486 - 6.63e-14    9590.823 - 6.94e-14    9641.962 - 4.32e-14
rtREV+I+G4        -4785.918    9593.835 - 1.24e-14    9594.172 -  1.3e-14    9645.311 -  8.1e-15
rtREV+G4          -4791.371    9602.742 - 1.45e-16    9603.022 - 1.56e-16    9649.538 - 9.79e-16
JTTDCMut+I+G4     -4789.487    9600.973 -  3.5e-16    9601.310 - 3.66e-16    9652.449 - 2.28e-16
Q.yeast+G4        -4793.400    9606.800 -  1.9e-17    9607.080 - 2.05e-17    9653.596 - 1.29e-16
Q.plant+G4        -4794.205    9608.410 -  8.5e-18    9608.691 - 9.15e-18    9655.206 - 5.75e-17
JTT+I+G4          -4791.124    9604.249 - 6.81e-17    9604.585 - 7.12e-17    9655.724 - 4.44e-17
JTTDCMut+G4       -4795.768    9611.536 - 1.78e-18    9611.816 - 1.92e-18    9658.332 -  1.2e-17
JTT+G4            -4797.364    9614.729 - 3.61e-19    9615.009 - 3.88e-19    9661.525 - 2.44e-18
Q.insect+I+G4     -4795.237    9612.475 - 1.11e-18    9612.811 - 1.17e-18    9663.950 - 7.26e-19
Blosum62+G4       -4799.131    9618.261 - 6.17e-20    9618.542 - 6.64e-20    9665.057 - 4.17e-19
Blosum62+I+G4     -4796.305    9614.610 - 3.83e-19    9614.947 - 4.01e-19    9666.086 -  2.5e-19
PMB+G4            -4804.054    9628.108 - 4.49e-22    9628.388 - 4.83e-22    9674.904 - 3.04e-21
Q.insect+G4       -4804.135    9628.271 - 4.14e-22    9628.551 - 4.45e-22    9675.067 -  2.8e-21
cpREV+F+I+G4      -4737.617    9535.233 +    0.066    9537.665 -   0.0242    9675.621 - 2.12e-21
cpREV+F+G4        -4741.076    9540.152 -  0.00564    9542.424 -  0.00224    9675.861 - 1.88e-21
PMB+I+G4          -4801.637    9625.275 - 1.85e-21    9625.612 - 1.94e-21    9676.750 - 1.21e-21
mtART+F+I+G4      -4744.357    9548.714 -  7.8e-05    9551.145 - 2.87e-05    9689.102 - 2.51e-24
mtZOA+F+I+G4      -4745.935    9551.870 - 1.61e-05    9554.301 - 5.91e-06    9692.258 - 5.18e-25
LG+F+I+G4         -4746.272    9552.545 - 1.15e-05    9554.976 - 4.22e-06    9692.933 - 3.69e-25
Dayhoff+I+G4      -4810.213    9642.426 - 3.49e-25    9642.763 - 3.65e-25    9693.902 - 2.28e-25
DCMut+I+G4        -4810.333    9642.665 -  3.1e-25    9643.002 - 3.24e-25    9694.141 - 2.02e-25
Dayhoff+G4        -4815.703    9651.406 - 3.92e-27    9651.687 - 4.22e-27    9698.202 - 2.65e-26
DCMut+G4          -4815.774    9651.549 - 3.65e-27    9651.829 - 3.93e-27    9698.345 - 2.47e-26
mtART+F+G4        -4752.459    9562.918 - 6.43e-08    9565.189 - 2.56e-08    9698.626 - 2.14e-26
LG+F+G4           -4752.616    9563.233 - 5.49e-08    9565.505 - 2.18e-08    9698.941 - 1.83e-26
mtZOA+F+G4        -4754.209    9566.418 - 1.12e-08    9568.689 - 4.44e-09    9702.126 - 3.73e-27
Q.pfam+F+I+G4     -4752.279    9564.559 - 2.83e-08    9566.990 - 1.04e-08    9704.947 - 9.09e-28
mtInv+F+I+G4      -4753.769    9567.537 - 6.38e-09    9569.969 - 2.34e-09    9707.925 - 2.05e-28
WAG+F+I+G4        -4753.892    9567.784 - 5.64e-09    9570.216 - 2.07e-09    9708.172 - 1.81e-28
WAG+F+G4          -4757.613    9573.227 - 3.71e-10    9575.498 - 1.48e-10    9708.935 - 1.24e-28
Q.pfam+F+G4       -4758.639    9575.278 - 1.33e-10    9577.550 - 5.29e-11    9710.987 - 4.44e-29
rtREV+F+I+G4      -4755.824    9571.649 - 8.16e-10    9574.080 -    3e-10    9712.037 - 2.62e-29
mtInv+F+G4        -4760.247    9578.495 - 2.66e-11    9580.766 - 1.06e-11    9714.203 - 8.89e-30
rtREV+F+G4        -4761.112    9580.224 - 1.12e-11    9582.496 - 4.46e-12    9715.933 - 3.74e-30
Q.yeast+F+I+G4    -4760.383    9580.766 - 8.55e-12    9583.198 - 3.14e-12    9721.154 - 2.75e-31
Q.plant+F+I+G4    -4762.682    9585.365 - 8.58e-13    9587.796 - 3.15e-13    9725.752 - 2.76e-32
mtREV+F+I+G4      -4763.806    9587.612 - 2.79e-13    9590.043 - 1.02e-13    9728.000 - 8.97e-33
Q.yeast+F+G4      -4768.290    9594.579 - 8.56e-15    9596.851 - 3.41e-15    9730.288 - 2.86e-33
mtREV+F+G4        -4770.353    9598.705 - 1.09e-15    9600.977 - 4.33e-16    9734.414 - 3.63e-34
VT+F+G4           -4771.629    9601.259 - 3.04e-16    9603.530 - 1.21e-16    9736.967 - 1.01e-34
Q.plant+F+G4      -4771.916    9601.831 - 2.28e-16    9604.103 - 9.07e-17    9737.540 - 7.61e-35
VT+F+I+G4         -4768.882    9597.764 - 1.74e-15    9600.196 -  6.4e-16    9738.152 -  5.6e-35
Q.insect+F+I+G4   -4768.958    9597.915 - 1.62e-15    9600.347 - 5.93e-16    9738.303 - 5.19e-35
Dayhoff+F+I+G4    -4769.817    9599.634 - 6.84e-16    9602.066 - 2.51e-16    9740.022 -  2.2e-35
DCMut+F+I+G4      -4769.947    9599.894 - 6.01e-16    9602.325 - 2.21e-16    9740.282 - 1.93e-35
JTTDCMut+F+I+G4   -4771.470    9602.940 - 1.31e-16    9605.371 - 4.81e-17    9743.328 - 4.21e-36
Dayhoff+F+G4      -4775.697    9609.395 - 5.19e-18    9611.666 - 2.07e-18    9745.103 - 1.73e-36
DCMut+F+G4        -4775.776    9609.552 -  4.8e-18    9611.823 - 1.91e-18    9745.260 -  1.6e-36
JTT+F+I+G4        -4773.051    9606.102 - 2.69e-17    9608.534 -  9.9e-18    9746.490 - 8.66e-37
Q.insect+F+G4     -4776.671    9611.342 - 1.96e-18    9613.614 -  7.8e-19    9747.050 - 6.55e-37
JTTDCMut+F+G4     -4777.486    9612.972 - 8.68e-19    9615.244 - 3.45e-19    9748.681 -  2.9e-37
HIVb+I+G4         -4838.442    9698.885 - 1.92e-37    9699.221 - 2.01e-37    9750.360 - 1.25e-37
PMB+F+G4          -4778.450    9614.900 - 3.31e-19    9617.172 - 1.32e-19    9750.609 - 1.11e-37
mtMet+F+I+G4      -4775.135    9610.270 - 3.35e-18    9612.702 - 1.23e-18    9750.658 - 1.08e-37
JTT+F+G4          -4778.998    9615.995 - 1.92e-19    9618.267 - 7.62e-20    9751.704 - 6.39e-38
PMB+F+I+G4        -4776.222    9612.445 - 1.13e-18    9614.876 - 4.15e-19    9752.833 - 3.63e-38
mtMet+F+G4        -4783.430    9624.859 - 2.28e-21    9627.131 - 9.06e-22    9760.568 -  7.6e-40
HIVb+G4           -4847.221    9714.442 - 8.03e-41    9714.722 - 8.65e-41    9761.238 - 5.44e-40
Blosum62+F+G4     -4784.806    9627.612 - 5.75e-22    9629.884 - 2.29e-22    9763.321 - 1.92e-40
Blosum62+F+I+G4   -4782.259    9624.519 -  2.7e-21    9626.950 - 9.92e-22    9764.907 - 8.68e-41
FLU+I+G4          -4845.984    9713.969 - 1.02e-40    9714.305 - 1.07e-40    9765.444 - 6.64e-41
Q.mammal+I+G4     -4850.226    9722.451 - 1.46e-42    9722.788 - 1.53e-42    9773.927 - 9.55e-43
Q.bird+I+G4       -4851.171    9724.342 - 5.69e-43    9724.679 - 5.95e-43    9775.818 - 3.71e-43
FLU+G4            -4856.287    9732.574 - 9.28e-45    9732.854 - 9.99e-45    9779.370 - 6.28e-44
Q.mammal+G4       -4860.506    9741.012 - 1.36e-46    9741.293 - 1.47e-46    9787.808 - 9.24e-46
Q.bird+G4         -4863.081    9746.163 - 1.04e-47    9746.443 - 1.12e-47    9792.959 - 7.03e-47
FLU+F+I+G4        -4803.431    9666.863 - 1.72e-30    9669.294 - 6.33e-31    9807.251 - 5.54e-50
mtMAM+F+I+G4      -4805.644    9671.288 - 1.89e-31    9673.719 - 6.93e-32    9811.676 - 6.07e-51
HIVb+F+I+G4       -4806.965    9673.931 - 5.03e-32    9676.362 - 1.85e-32    9814.319 - 1.62e-51
mtVer+F+I+G4      -4808.228    9676.457 - 1.42e-32    9678.888 - 5.23e-33    9816.845 - 4.58e-52
Q.mammal+F+I+G4   -4808.873    9677.746 - 7.47e-33    9680.178 - 2.74e-33    9818.134 -  2.4e-52
FLU+F+G4          -4812.951    9683.903 - 3.44e-34    9686.174 - 1.37e-34    9819.611 - 1.15e-52
HIVb+F+G4         -4815.656    9689.312 -  2.3e-35    9691.583 - 9.15e-36    9825.020 - 7.68e-54
mtMAM+F+G4        -4816.650    9691.300 - 8.51e-36    9693.572 - 3.39e-36    9827.009 - 2.84e-54
Q.mammal+F+G4     -4818.621    9695.242 - 1.19e-36    9697.514 - 4.72e-37    9830.951 - 3.96e-55
mtVer+F+G4        -4821.298    9700.595 - 8.16e-38    9702.867 - 3.25e-38    9836.303 - 2.72e-56
Q.bird+F+I+G4     -4818.887    9697.773 - 3.35e-37    9700.205 - 1.23e-37    9838.161 - 1.08e-56
Q.bird+F+G4       -4830.313    9718.627 - 9.91e-42    9720.898 - 3.94e-42    9854.335 - 3.31e-60
FLAVI+I+G4        -4909.651    9841.303 - 2.28e-68    9841.639 - 2.38e-68    9892.778 - 1.48e-68
FLAVI+F+I+G4      -4846.739    9753.479 - 2.68e-49    9755.910 - 9.84e-50    9893.867 - 8.62e-69
mtZOA+I+G4        -4912.946    9847.893 - 8.44e-70    9848.230 - 8.83e-70    9899.369 -  5.5e-70
mtZOA+G4          -4920.530    9861.060 - 1.17e-72    9861.341 - 1.26e-72    9907.856 -  7.9e-72
FLAVI+G4          -4921.129    9862.258 - 6.41e-73    9862.538 -  6.9e-73    9909.054 - 4.34e-72
FLAVI+F+G4        -4859.578    9777.156 - 1.93e-54    9779.428 - 7.69e-55    9912.865 - 6.46e-73
mtART+I+G4        -4936.533    9895.066 - 4.82e-80    9895.402 - 5.04e-80    9946.541 - 3.14e-80
mtART+G4          -4943.775    9907.550 - 9.38e-83    9907.830 - 1.01e-82    9954.346 - 6.35e-82
LG                -4967.424    9952.849 - 1.37e-92    9953.078 - 1.51e-92    9994.965 - 9.59e-91
HIVw+I+G4         -4961.731    9945.463 - 5.49e-91    9945.799 - 5.74e-91    9996.938 - 3.58e-91
mtREV+I+G4        -4965.210    9952.420 - 1.69e-92    9952.756 - 1.77e-92   10003.895 -  1.1e-92
mtREV+G4          -4973.869    9967.739 - 7.98e-96    9968.019 - 8.59e-96   10014.535 -  5.4e-95
HIVw+F+I+G4       -4907.718    9875.435 - 8.82e-76    9877.867 - 3.24e-76   10015.823 - 2.84e-95
mtMet+I+G4        -4971.840    9965.680 - 2.23e-95    9966.017 - 2.34e-95   10017.156 - 1.46e-95
HIVw+G4           -4977.561    9975.122 - 1.99e-97    9975.402 - 2.14e-97   10021.918 - 1.35e-96
mtMet+G4          -4981.233    9982.467 - 5.06e-99    9982.747 - 5.45e-99   10029.263 - 3.42e-98
HIVw+F+G4         -4922.673    9903.345 - 7.67e-82    9905.617 - 3.05e-82   10039.054 - 2.56e-100
mtMAM+I+G4        -5003.477   10028.953 - 4.07e-109   10029.290 - 4.26e-109   10080.429 - 2.65e-109
mtInv+I+G4        -5006.654   10035.307 - 1.7e-110   10035.644 - 1.78e-110   10086.783 - 1.11e-110
mtMAM+G4          -5012.493   10044.986 - 1.34e-112   10045.266 - 1.45e-112   10091.782 - 9.09e-112
mtVer+I+G4        -5009.382   10040.765 - 1.11e-111   10041.102 - 1.16e-111   10092.240 - 7.23e-112
mtInv+G4          -5015.079   10050.159 - 1.01e-113   10050.439 - 1.09e-113   10096.955 - 6.84e-113
mtVer+G4          -5020.644   10061.287 - 3.88e-116   10061.567 - 4.17e-116   10108.083 - 2.62e-115

AIC, w-AIC   : Akaike information criterion scores and weights.
AICc, w-AICc : Corrected AIC scores and weights.
BIC, w-BIC   : Bayesian information criterion scores and weights.

Plus signs denote the 95% confidence sets.
Minus signs denote significant exclusion.

SUBSTITUTION PROCESS
--------------------

Model of substitution: cpREV+I+G4

State frequencies: (model)

Model of rate heterogeneity: Invar+Gamma with 4 categories
Proportion of invariable sites: 0.4626
Gamma shape alpha: 0.7873

 Category  Relative_rate  Proportion
  0         0              0.4626
  1         0.1728         0.1344
  2         0.7480         0.1344
  3         1.7740         0.1344
  4         4.7484         0.1344
Relative rates are computed as MEAN of the portion of the Gamma distribution falling in the category.

MAXIMUM LIKELIHOOD TREE
-----------------------

Log-likelihood of the tree: -4753.6149 (s.e. 129.2064)
Unconstrained log-likelihood (without tree): -3410.0651
Number of free parameters (#branches + #model parameters): 11
Akaike information criterion (AIC) score: 9529.2298
Corrected Akaike information criterion (AICc) score: 9529.5665
Bayesian information criterion (BIC) score: 9580.7054

Total tree length (sum of branch lengths): 0.8705
Sum of internal branch lengths: 0.2131 (24.4767% of tree length)

NOTE: Tree is UNROOTED although outgroup taxon 'Avrainvillea_mazei_HV02664' is drawn at root
Numbers in parentheses are  ultrafast bootstrap support (%)

+---------------------------------------------Avrainvillea_mazei_HV02664
|
|                 +----------------------Bryopsis_plumosa_WEST4718
+-----------------| (98)
|                 +-------------------------------------Derbesia_sp_WEST4838
|
|                        +---------------------------------Caulerpa_cliftonii_HV03798
+------------------------| (100)
                         |          +---------------------Chlorodesmis_fastigiata_HV03865
                         +----------| (92)
                                    +-----Flabellia_petiolata_HV01202

Tree in newick format:

(Avrainvillea_mazei_HV02664:0.1774010564,(Bryopsis_plumosa_WEST4718:0.0911761379,Derbesia_sp_WEST4838:0.1476272926)98:0.0699127956,(Caulerpa_cliftonii_HV03798:0.1301703118,(Chlorodesmis_fastigiata_HV03865:0.0865187215,Flabellia_petiolata_HV01202:0.0245104903)92:0.0442681486)100:0.0988799497);

CONSENSUS TREE
--------------

Consensus tree is constructed from 1000 bootstrap trees
Log-likelihood of consensus tree: -4753.614908
Robinson-Foulds distance between ML tree and consensus tree: 0

Branches with support >0.000000% are kept (extended consensus)
Branch lengths are optimized by maximum likelihood on original alignment
Numbers in parentheses are bootstrap supports (%)

+---------------------------------------------Avrainvillea_mazei_HV02664
|
|                 +----------------------Bryopsis_plumosa_WEST4718
+-----------------| (98)
|                 +-------------------------------------Derbesia_sp_WEST4838
|
|                        +---------------------------------Caulerpa_cliftonii_HV03798
+------------------------| (100)
                         |          +---------------------Chlorodesmis_fastigiata_HV03865
                         +----------| (92)
                                    +-----Flabellia_petiolata_HV01202


Consensus tree in newick format: 

(Avrainvillea_mazei_HV02664:0.1774144310,(Bryopsis_plumosa_WEST4718:0.0911750125,Derbesia_sp_WEST4838:0.1476344869)98:0.0699199323,(Caulerpa_cliftonii_HV03798:0.1302020010,(Chlorodesmis_fastigiata_HV03865:0.0865481861,Flabellia_petiolata_HV01202:0.0244952561)92:0.0442550027)100:0.0988875469);

TIME STAMP
----------

Date and time: Sat Dec 16 03:42:26 2023
Total CPU time used: 956.280323 seconds (0h:15m:56s)
Total wall-clock time used: 74.05670908 seconds (0h:1m:14s)

IQ-TREE multicore version 2.2.0.3 COVID-edition for Linux 64-bit built Aug  2 2022
Developed by Bui Quang Minh, James Barbetti, Nguyen Lam Tung,
Olga Chernomor, Heiko Schmidt, Dominik Schrempf, Michael Woodhams, Ly Trong Nhan.

Host:    herbairumphylo (AVX512, FMA3, 283 GB RAM)
Command: iqtree2 -s results/supermatrix/supermatrix.protein.fa -bb 1000 -m TEST -nt 32 -redo
Seed:    413450 (Using SPRNG - Scalable Parallel Random Number Generator)
Time:    Sat Dec 16 03:40:29 2023
Kernel:  AVX+FMA - 32 threads (32 CPU cores detected)

Reading alignment file results/supermatrix/supermatrix.protein.fa ... Fasta format detected
Reading fasta file: done in 0.000102289 secs
Alignment most likely contains protein sequences
Constructing alignment: done in 0.0012522 secs using 291.5% CPU
Alignment has 6 sequences with 796 columns, 255 distinct patterns
100 parsimony-informative, 143 singleton sites, 553 constant sites
                                 Gap/Ambiguity  Composition  p-value
Analyzing sequences: done in 0.00294819 secs using 4133% CPU
   1  Avrainvillea_mazei_HV02664         1.13%    passed     99.95%
   2  Bryopsis_plumosa_WEST4718          0.38%    passed    100.00%
   3  Caulerpa_cliftonii_HV03798         0.00%    passed    100.00%
   4  Chlorodesmis_fastigiata_HV03865    1.51%    passed    100.00%
   5  Derbesia_sp_WEST4838               0.63%    passed    100.00%
   6  Flabellia_petiolata_HV01202        2.39%    passed    100.00%
****  TOTAL                              1.01%  0 sequences failed composition chi2 test (p-value<5%; df=19)
Checking for duplicate sequences: done in 0.00191325 secs using 624.9% CPU

WARNING: 32 threads for alignment length 255 will slow down analysis

Create initial parsimony tree by phylogenetic likelihood library (PLL)... 0.000 seconds
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
Perform fast likelihood tree search using LG+I+G model...
Estimate model parameters (epsilon = 5.000)
Perform nearest neighbor interchange...
Optimizing NNI: done in 0.00980802 secs using 2466% CPU
Estimate model parameters (epsilon = 1.000)
1. Initial log-likelihood: -4763.107
Optimal log-likelihood: -4763.040
Proportion of invariable sites: 0.357
Gamma shape alpha: 0.456
Parameters optimization took 1 rounds (0.008 sec)
Time for fast ML tree search: 0.055 seconds

NOTE: ModelFinder requires 2 MB RAM!
ModelFinder will test up to 224 protein models (sample size: 796) ...
 No. Model         -LnL         df  AIC          AICc         BIC
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
  1  LG            4967.424     9   9952.849     9953.078     9994.965
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
  2  LG+I          4785.119     10  9590.237     9590.517     9637.033
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
  3  LG+G4         4769.787     10  9559.575     9559.855     9606.371
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
  4  LG+I+G4       4762.923     11  9547.846     9548.183     9599.322
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
  7  LG+F+G4       4752.616     29  9563.233     9565.505     9698.941
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
  8  LG+F+I+G4     4746.272     30  9552.545     9554.976     9692.933
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 11  WAG+G4        4782.001     10  9584.002     9584.283     9630.798
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 12  WAG+I+G4      4778.199     11  9578.397     9578.734     9629.873
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 15  WAG+F+G4      4757.613     29  9573.227     9575.498     9708.935
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 16  WAG+F+I+G4    4753.892     30  9567.784     9570.216     9708.172
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 19  JTT+G4        4797.364     10  9614.729     9615.009     9661.525
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 20  JTT+I+G4      4791.124     11  9604.249     9604.585     9655.724
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 23  JTT+F+G4      4778.998     29  9615.995     9618.267     9751.704
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 24  JTT+F+I+G4    4773.051     30  9606.102     9608.534     9746.490
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 27  Q.pfam+G4     4779.840     10  9579.680     9579.960     9626.476
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 28  Q.pfam+I+G4   4772.958     11  9567.915     9568.252     9619.391
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 31  Q.pfam+F+G4   4758.639     29  9575.278     9577.550     9710.987
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 32  Q.pfam+F+I+G4 4752.279     30  9564.559     9566.990     9704.947
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 35  Q.bird+G4     4863.081     10  9746.163     9746.443     9792.959
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 36  Q.bird+I+G4   4851.171     11  9724.342     9724.679     9775.818
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 39  Q.bird+F+G4   4830.313     29  9718.627     9720.898     9854.335
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 40  Q.bird+F+I+G4 4818.887     30  9697.773     9700.205     9838.161
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 43  Q.mammal+G4   4860.506     10  9741.012     9741.293     9787.808
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 44  Q.mammal+I+G4 4850.226     11  9722.451     9722.788     9773.927
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 47  Q.mammal+F+G4 4818.621     29  9695.242     9697.514     9830.951
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 48  Q.mammal+F+I+G4 4808.873     30  9677.746     9680.178     9818.134
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 51  Q.insect+G4   4804.135     10  9628.271     9628.551     9675.067
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 52  Q.insect+I+G4 4795.237     11  9612.475     9612.811     9663.950
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 55  Q.insect+F+G4 4776.671     29  9611.342     9613.614     9747.050
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 56  Q.insect+F+I+G4 4768.958     30  9597.915     9600.347     9738.303
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 59  Q.plant+G4    4794.205     10  9608.410     9608.691     9655.206
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 60  Q.plant+I+G4  4784.005     11  9590.009     9590.346     9641.485
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 63  Q.plant+F+G4  4771.916     29  9601.831     9604.103     9737.540
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 64  Q.plant+F+I+G4 4762.682     30  9585.365     9587.796     9725.752
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 67  Q.yeast+G4    4793.400     10  9606.800     9607.080     9653.596
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 68  Q.yeast+I+G4  4784.243     11  9590.486     9590.823     9641.962
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 71  Q.yeast+F+G4  4768.290     29  9594.579     9596.851     9730.288
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 72  Q.yeast+F+I+G4 4760.383     30  9580.766     9583.198     9721.154
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 75  JTTDCMut+G4   4795.768     10  9611.536     9611.816     9658.332
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 76  JTTDCMut+I+G4 4789.487     11  9600.973     9601.310     9652.449
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 79  JTTDCMut+F+G4 4777.486     29  9612.972     9615.244     9748.681
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 80  JTTDCMut+F+I+G4 4771.470     30  9602.940     9605.371     9743.328
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 83  DCMut+G4      4815.774     10  9651.549     9651.829     9698.345
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 84  DCMut+I+G4    4810.333     11  9642.665     9643.002     9694.141
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 87  DCMut+F+G4    4775.776     29  9609.552     9611.823     9745.260
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 88  DCMut+F+I+G4  4769.947     30  9599.894     9602.325     9740.282
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 91  VT+G4         4783.028     10  9586.055     9586.335     9632.851
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 92  VT+I+G4       4779.908     11  9581.816     9582.153     9633.291
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 95  VT+F+G4       4771.629     29  9601.259     9603.530     9736.967
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 96  VT+F+I+G4     4768.882     30  9597.764     9600.196     9738.152
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 99  PMB+G4        4804.054     10  9628.108     9628.388     9674.904
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
100  PMB+I+G4      4801.637     11  9625.275     9625.612     9676.750
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
103  PMB+F+G4      4778.450     29  9614.900     9617.172     9750.609
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
104  PMB+F+I+G4    4776.222     30  9612.445     9614.876     9752.833
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
107  Blosum62+G4   4799.131     10  9618.261     9618.542     9665.057
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
108  Blosum62+I+G4 4796.305     11  9614.610     9614.947     9666.086
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
111  Blosum62+F+G4 4784.806     29  9627.612     9629.884     9763.321
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
112  Blosum62+F+I+G4 4782.259     30  9624.519     9626.950     9764.907
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
115  Dayhoff+G4    4815.703     10  9651.406     9651.687     9698.202
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
116  Dayhoff+I+G4  4810.213     11  9642.426     9642.763     9693.902
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
119  Dayhoff+F+G4  4775.697     29  9609.395     9611.666     9745.103
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
120  Dayhoff+F+I+G4 4769.817     30  9599.634     9602.066     9740.022
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
123  mtREV+G4      4973.869     10  9967.739     9968.019     10014.535
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
124  mtREV+I+G4    4965.210     11  9952.420     9952.756     10003.895
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
127  mtREV+F+G4    4770.353     29  9598.705     9600.977     9734.414
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
128  mtREV+F+I+G4  4763.806     30  9587.612     9590.043     9728.000
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
131  mtART+G4      4943.775     10  9907.550     9907.830     9954.346
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
132  mtART+I+G4    4936.533     11  9895.066     9895.402     9946.541
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
135  mtART+F+G4    4752.459     29  9562.918     9565.189     9698.626
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
136  mtART+F+I+G4  4744.357     30  9548.714     9551.145     9689.102
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
139  mtZOA+G4      4920.530     10  9861.060     9861.341     9907.856
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
140  mtZOA+I+G4    4912.946     11  9847.893     9848.230     9899.369
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
143  mtZOA+F+G4    4754.209     29  9566.418     9568.689     9702.126
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
144  mtZOA+F+I+G4  4745.935     30  9551.870     9554.301     9692.258
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
147  mtMet+G4      4981.233     10  9982.467     9982.747     10029.263
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
148  mtMet+I+G4    4971.840     11  9965.680     9966.017     10017.156
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
151  mtMet+F+G4    4783.430     29  9624.859     9627.131     9760.568
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
152  mtMet+F+I+G4  4775.135     30  9610.270     9612.702     9750.658
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
155  mtVer+G4      5020.644     10  10061.287    10061.567    10108.083
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
156  mtVer+I+G4    5009.382     11  10040.765    10041.102    10092.240
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
159  mtVer+F+G4    4821.298     29  9700.595     9702.867     9836.303
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
160  mtVer+F+I+G4  4808.228     30  9676.457     9678.888     9816.845
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
163  mtInv+G4      5015.079     10  10050.159    10050.439    10096.955
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
164  mtInv+I+G4    5006.654     11  10035.307    10035.644    10086.783
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
167  mtInv+F+G4    4760.247     29  9578.495     9580.766     9714.203
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
168  mtInv+F+I+G4  4753.769     30  9567.537     9569.969     9707.925
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
171  mtMAM+G4      5012.493     10  10044.986    10045.266    10091.782
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
172  mtMAM+I+G4    5003.477     11  10028.953    10029.290    10080.429
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
175  mtMAM+F+G4    4816.650     29  9691.300     9693.572     9827.009
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
176  mtMAM+F+I+G4  4805.644     30  9671.288     9673.719     9811.676
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
179  FLAVI+G4      4921.129     10  9862.258     9862.538     9909.054
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
180  FLAVI+I+G4    4909.651     11  9841.303     9841.639     9892.778
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
183  FLAVI+F+G4    4859.578     29  9777.156     9779.428     9912.865
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Normalizing state frequencies so that sum of them equals to 1
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
184  FLAVI+F+I+G4  4846.739     30  9753.479     9755.910     9893.867
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
187  HIVb+G4       4847.221     10  9714.442     9714.722     9761.238
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
188  HIVb+I+G4     4838.442     11  9698.885     9699.221     9750.360
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
191  HIVb+F+G4     4815.656     29  9689.312     9691.583     9825.020
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
192  HIVb+F+I+G4   4806.965     30  9673.931     9676.362     9814.319
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
195  HIVw+G4       4977.561     10  9975.122     9975.402     10021.918
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
196  HIVw+I+G4     4961.731     11  9945.463     9945.799     9996.938
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
199  HIVw+F+G4     4922.673     29  9903.345     9905.617     10039.054
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
200  HIVw+F+I+G4   4907.718     30  9875.435     9877.867     10015.823
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
203  FLU+G4        4856.287     10  9732.574     9732.854     9779.370
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
204  FLU+I+G4      4845.984     11  9713.969     9714.305     9765.444
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
207  FLU+F+G4      4812.951     29  9683.903     9686.174     9819.611
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
208  FLU+F+I+G4    4803.431     30  9666.863     9669.294     9807.251
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
211  rtREV+G4      4791.371     10  9602.742     9603.022     9649.538
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
212  rtREV+I+G4    4785.918     11  9593.835     9594.172     9645.311
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
215  rtREV+F+G4    4761.112     29  9580.224     9582.496     9715.933
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
216  rtREV+F+I+G4  4755.824     30  9571.649     9574.080     9712.037
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
219  cpREV+G4      4757.639     10  9535.278     9535.558     9582.074
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
220  cpREV+I+G4    4754.045     11  9530.090     9530.427     9581.566
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
223  cpREV+F+G4    4741.076     29  9540.152     9542.424     9675.861
WARNING: 32 threads for alignment length 255 will slow down analysis
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
224  cpREV+F+I+G4  4737.617     30  9535.233     9537.665     9675.621
Akaike Information Criterion:           cpREV+I+G4
Corrected Akaike Information Criterion: cpREV+I+G4
Bayesian Information Criterion:         cpREV+I+G4
Best-fit model: cpREV+I+G4 chosen according to BIC

All model information printed to results/supermatrix/supermatrix.protein.fa.model.gz
CPU time for ModelFinder: 423.657 seconds (0h:7m:3s)
Wall-clock time for ModelFinder: 42.930 seconds (0h:0m:42s)
Generating 1000 samples for ultrafast bootstrap (seed: 413450)...
WARNING: 32 threads for alignment length 255 will slow down analysis

NOTE: 2 MB RAM (0 GB) is required!
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
Estimate model parameters (epsilon = 0.100)
Thoroughly optimizing +I+G parameters from 10 start values...
Init pinv, alpha: 0.000, 0.547 / Estimate: 0.000, 0.237 / LogL: -4757.639
Init pinv, alpha: 0.077, 0.547 / Estimate: 0.080, 0.273 / LogL: -4756.785
Init pinv, alpha: 0.154, 0.547 / Estimate: 0.158, 0.320 / LogL: -4755.996
Init pinv, alpha: 0.232, 0.547 / Estimate: 0.236, 0.381 / LogL: -4755.220
Init pinv, alpha: 0.309, 0.547 / Estimate: 0.314, 0.465 / LogL: -4754.490
Init pinv, alpha: 0.386, 0.547 / Estimate: 0.390, 0.589 / LogL: -4753.890
Init pinv, alpha: 0.463, 0.547 / Estimate: 0.463, 0.787 / LogL: -4753.615
Init pinv, alpha: 0.540, 0.547 / Estimate: 0.524, 1.098 / LogL: -4753.966
Init pinv, alpha: 0.618, 0.547 / Estimate: 0.531, 1.143 / LogL: -4754.058
Init pinv, alpha: 0.695, 0.547 / Estimate: 0.528, 1.124 / LogL: -4754.018
Optimal pinv,alpha: 0.463, 0.787 / LogL: -4753.615

Parameters optimization took 0.226 sec
Wrote distance file to... 
Computing ML distances based on estimated model parameters...
Calculating distance matrix: done in 0.00506882 secs using 2506% CPU
Computing ML distances took 0.014985 sec (of wall-clock time) 0.403967 sec (of CPU time)
Setting up auxiliary I and S matrices: done in 0.0115662 secs using 10.94% CPU
Constructing RapidNJ tree: done in 0.0318284 secs using 125.4% CPU
Computing RapidNJ tree took 0.043559 sec (of wall-clock time) 0.043260 sec (of CPU time)
Log-likelihood of RapidNJ tree: -4753.615
--------------------------------------------------------------------
|             INITIALIZING CANDIDATE TREE SET                      |
--------------------------------------------------------------------
Generating 99 parsimony trees... 0.031 second
Computing log-likelihood of 6 initial trees ... 1.552 seconds
Current best score: -4753.615

Do NNI search on 7 best initial trees
Optimizing NNI: done in 0.720347 secs using 1784% CPU
Optimizing NNI: done in 0.138424 secs using 2913% CPU
Optimizing NNI: done in 0.165715 secs using 3002% CPU
Optimizing NNI: done in 1.47911 secs using 1114% CPU
Optimizing NNI: done in 1.08875 secs using 1957% CPU
Optimizing NNI: done in 0.149227 secs using 2978% CPU
Optimizing NNI: done in 0.133209 secs using 3047% CPU
Finish initializing candidate tree set (7)
Current best tree score: -4753.615 / CPU time: 5.612
Number of iterations: 7
--------------------------------------------------------------------
|               OPTIMIZING CANDIDATE TREE SET                      |
--------------------------------------------------------------------
Optimizing NNI: done in 0.135266 secs using 2953% CPU
Optimizing NNI: done in 0.0985645 secs using 3003% CPU
Optimizing NNI: done in 0.214924 secs using 2640% CPU
UPDATE BEST LOG-LIKELIHOOD: -4753.615
Iteration 10 / LogL: -4753.615 / Time: 0h:0m:6s
Optimizing NNI: done in 0.0817527 secs using 2936% CPU
Optimizing NNI: done in 0.755061 secs using 1910% CPU
Optimizing NNI: done in 1.48548 secs using 1452% CPU
Optimizing NNI: done in 1.27148 secs using 1544% CPU
Optimizing NNI: done in 0.771219 secs using 1744% CPU
Optimizing NNI: done in 0.0794607 secs using 2685% CPU
Optimizing NNI: done in 0.0861665 secs using 3023% CPU
Optimizing NNI: done in 0.123241 secs using 2993% CPU
Optimizing NNI: done in 0.0443138 secs using 2966% CPU
Optimizing NNI: done in 0.08832 secs using 3056% CPU
Iteration 20 / LogL: -4753.678 / Time: 0h:0m:11s
Optimizing NNI: done in 0.10284 secs using 3008% CPU
Optimizing NNI: done in 0.111081 secs using 3021% CPU
Optimizing NNI: done in 0.0957037 secs using 3059% CPU
Optimizing NNI: done in 0.126833 secs using 3030% CPU
Optimizing NNI: done in 0.0804382 secs using 2655% CPU
Optimizing NNI: done in 0.105339 secs using 3015% CPU
Optimizing NNI: done in 0.11242 secs using 3048% CPU
Optimizing NNI: done in 0.109235 secs using 3005% CPU
Optimizing NNI: done in 0.0791539 secs using 2993% CPU
UPDATE BEST LOG-LIKELIHOOD: -4753.615
Optimizing NNI: done in 0.0688421 secs using 3098% CPU
Iteration 30 / LogL: -4753.632 / Time: 0h:0m:12s (0h:0m:30s left)
Optimizing NNI: done in 0.0728304 secs using 3031% CPU
Optimizing NNI: done in 0.0832651 secs using 3028% CPU
Optimizing NNI: done in 0.0737372 secs using 3105% CPU
Optimizing NNI: done in 0.0763942 secs using 2982% CPU
Optimizing NNI: done in 0.108216 secs using 3027% CPU
Optimizing NNI: done in 0.110627 secs using 3020% CPU
Optimizing NNI: done in 0.0899857 secs using 2984% CPU
Optimizing NNI: done in 0.105611 secs using 2898% CPU
Optimizing NNI: done in 0.118817 secs using 3024% CPU
Optimizing NNI: done in 0.9553 secs using 945% CPU
Iteration 40 / LogL: -4761.550 / Time: 0h:0m:14s (0h:0m:22s left)
Optimizing NNI: done in 0.336211 secs using 2384% CPU
Optimizing NNI: done in 0.944855 secs using 1392% CPU
Optimizing NNI: done in 0.411638 secs using 1397% CPU
Optimizing NNI: done in 1.47347 secs using 1346% CPU
Optimizing NNI: done in 2.25614 secs using 1169% CPU
Optimizing NNI: done in 0.111458 secs using 3026% CPU
Optimizing NNI: done in 0.0819072 secs using 3004% CPU
Optimizing NNI: done in 1.30871 secs using 1288% CPU
Optimizing NNI: done in 2.59779 secs using 1165% CPU
Optimizing NNI: done in 0.0978691 secs using 3042% CPU
Iteration 50 / LogL: -4753.643 / Time: 0h:0m:24s (0h:0m:25s left)
Log-likelihood cutoff on original alignment: -4776.240
Optimizing NNI: done in 0.0846922 secs using 3068% CPU
Optimizing NNI: done in 4.27952 secs using 735.3% CPU
Optimizing NNI: done in 1.73447 secs using 786.6% CPU
Optimizing NNI: done in 0.341978 secs using 2777% CPU
Optimizing NNI: done in 0.0830223 secs using 3068% CPU
Optimizing NNI: done in 0.114455 secs using 2943% CPU
Optimizing NNI: done in 0.0535858 secs using 3050% CPU
Optimizing NNI: done in 0.152817 secs using 2473% CPU
Optimizing NNI: done in 1.14794 secs using 1303% CPU
Optimizing NNI: done in 0.354029 secs using 1637% CPU
Iteration 60 / LogL: -4759.702 / Time: 0h:0m:33s (0h:0m:22s left)
Optimizing NNI: done in 1.70927 secs using 1389% CPU
Optimizing NNI: done in 2.61792 secs using 1103% CPU
Optimizing NNI: done in 0.186399 secs using 2015% CPU
Optimizing NNI: done in 0.739696 secs using 1632% CPU
Optimizing NNI: done in 2.75194 secs using 967.1% CPU
Optimizing NNI: done in 0.78255 secs using 2496% CPU
Optimizing NNI: done in 0.131722 secs using 2975% CPU
UPDATE BEST LOG-LIKELIHOOD: -4753.615
Optimizing NNI: done in 0.108147 secs using 3003% CPU
Optimizing NNI: done in 0.081633 secs using 2962% CPU
UPDATE BEST LOG-LIKELIHOOD: -4753.615
Optimizing NNI: done in 5.14366 secs using 689.1% CPU
Iteration 70 / LogL: -4753.673 / Time: 0h:0m:47s (0h:0m:20s left)
Optimizing NNI: done in 1.83514 secs using 795.2% CPU
Optimizing NNI: done in 1.73305 secs using 825.1% CPU
Optimizing NNI: done in 2.49018 secs using 582% CPU
Optimizing NNI: done in 3.64304 secs using 824.8% CPU
Optimizing NNI: done in 3.00808 secs using 997.9% CPU
Optimizing NNI: done in 1.32291 secs using 2023% CPU
Optimizing NNI: done in 0.0874452 secs using 3072% CPU
Optimizing NNI: done in 0.092638 secs using 3050% CPU
Optimizing NNI: done in 0.0809316 secs using 3018% CPU
Optimizing NNI: done in 0.0858965 secs using 3042% CPU
Iteration 80 / LogL: -4753.617 / Time: 0h:1m:2s (0h:0m:15s left)
Optimizing NNI: done in 0.0696904 secs using 3009% CPU
Optimizing NNI: done in 0.135512 secs using 2950% CPU
Optimizing NNI: done in 0.0813792 secs using 3030% CPU
Optimizing NNI: done in 0.0807351 secs using 3011% CPU
Optimizing NNI: done in 0.0988241 secs using 3014% CPU
Optimizing NNI: done in 0.0910111 secs using 2972% CPU
Optimizing NNI: done in 0.0828082 secs using 2680% CPU
Optimizing NNI: done in 0.0791119 secs using 3042% CPU
Optimizing NNI: done in 0.0712579 secs using 3038% CPU
Optimizing NNI: done in 0.0714126 secs using 3081% CPU
Iteration 90 / LogL: -4753.624 / Time: 0h:1m:3s (0h:0m:7s left)
Optimizing NNI: done in 0.751056 secs using 1381% CPU
Optimizing NNI: done in 0.072715 secs using 3051% CPU
Optimizing NNI: done in 4.53437 secs using 769.2% CPU
Optimizing NNI: done in 1.33901 secs using 2243% CPU
Optimizing NNI: done in 0.410689 secs using 2853% CPU
Optimizing NNI: done in 0.0802805 secs using 3071% CPU
Optimizing NNI: done in 0.0876853 secs using 3048% CPU
Optimizing NNI: done in 0.0875309 secs using 2935% CPU
Optimizing NNI: done in 0.10691 secs using 2995% CPU
Optimizing NNI: done in 0.0810742 secs using 3040% CPU
Iteration 100 / LogL: -4753.615 / Time: 0h:1m:11s (0h:0m:0s left)
Log-likelihood cutoff on original alignment: -4776.240
NOTE: Bootstrap correlation coefficient of split occurrence frequencies: 1.000
Optimizing NNI: done in 0.169753 secs using 2607% CPU
TREE SEARCH COMPLETED AFTER 101 ITERATIONS / Time: 0h:1m:11s

--------------------------------------------------------------------
|                    FINALIZING TREE SEARCH                        |
--------------------------------------------------------------------
Performs final model parameters optimization
Estimate model parameters (epsilon = 0.010)
1. Initial log-likelihood: -4753.615
Optimal log-likelihood: -4753.615
Proportion of invariable sites: 0.463
Gamma shape alpha: 0.787
Parameters optimization took 1 rounds (0.256 sec)
BEST SCORE FOUND : -4753.615
Creating bootstrap support values...
Split supports printed to NEXUS file results/supermatrix/supermatrix.protein.fa.splits.nex
Total tree length: 0.870

Total number of iterations: 101
CPU time used for tree search: 942.632 sec (0h:15m:42s)
Wall-clock time used for tree search: 71.376 sec (0h:1m:11s)
Total CPU time used: 956.280 sec (0h:15m:56s)
Total wall-clock time used: 72.023 sec (0h:1m:12s)

Computing bootstrap consensus tree...
Reading input file results/supermatrix/supermatrix.protein.fa.splits.nex...
6 taxa and 13 splits.
Consensus tree written to results/supermatrix/supermatrix.protein.fa.contree
Reading input trees file results/supermatrix/supermatrix.protein.fa.contree
Log-likelihood of consensus tree: -4753.615

Analysis results written to: 
  IQ-TREE report:                results/supermatrix/supermatrix.protein.fa.iqtree
  Maximum-likelihood tree:       results/supermatrix/supermatrix.protein.fa.treefile
  Likelihood distances:          results/supermatrix/supermatrix.protein.fa.mldist

Ultrafast bootstrap approximation results written to:
  Split support values:          results/supermatrix/supermatrix.protein.fa.splits.nex
  Consensus tree:                results/supermatrix/supermatrix.protein.fa.contree
  Screen log file:               results/supermatrix/supermatrix.protein.fa.log

ALISIM COMMAND
--------------
--alisim simulated_MSA -t results/supermatrix/supermatrix.protein.fa.treefile -m "cpREV+I{0.462593}+G4{0.787278}" --length 796

Date and Time: Sat Dec 16 03:42:26 2023

@article{10.1093/molbev/mst010,
    author = "Katoh, Kazutaka and Standley, Daron M.",
    title = "{MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability}",
    journal = "Molecular Biology and Evolution",
    volume = "30",
    number = "4",
    pages = "772-780",
    year = "2013",
    month = "01",
    abstract = "{We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.}",
    issn = "0737-4038",
    doi = "10.1093/molbev/mst010",
    url = "https://doi.org/10.1093/molbev/mst010",
    eprint = "https://academic.oup.com/mbe/article-pdf/30/4/772/6420419/mst010.pdf"
}

@article{Emms2019,
    author = "Emms, David M. and Kelly, Steven",
    type = "Journal Article",
    title = "OrthoFinder: phylogenetic orthology inference for comparative genomics",
    journal = "Genome Biology",
    number = "1",
    doi = "10.1186/s13059-019-1832-y",
    volume = "20",
    pages = "238",
    url = "https://doi.org/10.1186/s13059-019-1832-y",
    year = "2019",
    abstract = "Here, we present a major advance of the OrthoFinder method. This extends OrthoFinder’s high accuracy orthogroup inference to provide phylogenetic inference of orthologs, rooted gene trees, gene duplication events, the rooted species tree, and comparative genomics statistics. Each output is benchmarked on appropriate real or simulated datasets, and where comparable methods exist, OrthoFinder is equivalent to or outperforms these methods. Furthermore, OrthoFinder is the most accurate ortholog inference method on the Quest for Orthologs benchmark test. Finally, OrthoFinder’s comprehensive phylogenetic analysis is achieved with equivalent speed and scalability to the fastest, score-based heuristic methods. OrthoFinder is available at https://github.com/davidemms/OrthoFinder.",
    isbn = "1474-760X",
    DA = "2019/11/14"
}

@article{10.1371/journal.pbio.3001007,
    author = "Steenwyk, Jacob L. and Buida, III, Thomas J. and Li, Yuanning and Shen, Xing-Xing and Rokas, Antonis",
    doi = "10.1371/journal.pbio.3001007",
    journal = "PLOS Biology",
    publisher = "Public Library of Science",
    title = "ClipKIT: A multiple sequence alignment trimming software for accurate phylogenomic inference",
    year = "2020",
    month = "12",
    volume = "18",
    url = "https://doi.org/10.1371/journal.pbio.3001007",
    pages = "1-17",
    abstract = "Highly divergent sites in multiple sequence alignments (MSAs), which can stem from erroneous inference of homology and saturation of substitutions, are thought to negatively impact phylogenetic inference. Thus, several different trimming strategies have been developed for identifying and removing these sites prior to phylogenetic inference. However, a recent study reported that doing so can worsen inference, underscoring the need for alternative alignment trimming strategies. Here, we introduce ClipKIT, an alignment trimming software that, rather than identifying and removing putatively phylogenetically uninformative sites, instead aims to identify and retain parsimony-informative sites, which are known to be phylogenetically informative. To test the efficacy of ClipKIT, we examined the accuracy and support of phylogenies inferred from 14 different alignment trimming strategies, including those implemented in ClipKIT, across nearly 140,000 alignments from a broad sampling of evolutionary histories. Phylogenies inferred from ClipKIT-trimmed alignments are accurate, robust, and time saving. Furthermore, ClipKIT consistently outperformed other trimming methods across diverse datasets, suggesting that strategies based on identifying and retaining parsimony-informative sites provide a robust framework for alignment trimming.",
    number = "12"
}

@article{Steenwyk_2021,
    author = "Steenwyk, Jacob L. and Buida, Thomas J. and Gon{\c{c}}alves, Carla and Goltz, Dayna C. and Morales, Grace and Mead, Matthew E. and LaBella, Abigail L. and Chavez, Christina M. and Schmitz, Jonathan E. and Hadjifrangiskou, Maria and Li, Yuanning and Rokas, Antonis",
    doi = "10.1101/2021.10.02.462868",
    url = "https://doi.org/10.1101\%2F2021.10.02.462868",
    year = "2021",
    month = "oct",
    journal = "biorxiv",
    publisher = "Cold Spring Harbor Laboratory",
    title = "{BioKIT}: a versatile toolkit for processing and analyzing diverse types of sequence data"
}

@article{10.1093/molbev/msx281,
    author = "Hoang, Diep Thi and Chernomor, Olga and von Haeseler, Arndt and Minh, Bui Quang and Vinh, Le Sy",
    title = "{UFBoot2: Improving the Ultrafast Bootstrap Approximation}",
    journal = "Molecular Biology and Evolution",
    volume = "35",
    number = "2",
    pages = "518-522",
    year = "2017",
    month = "10",
    abstract = "{The standard bootstrap (SBS), despite being computationally intensive, is widely used in maximum likelihood phylogenetic analyses. We recently proposed the ultrafast bootstrap approximation (UFBoot) to reduce computing time while achieving more unbiased branch supports than SBS under mild model violations. UFBoot has been steadily adopted as an efficient alternative to SBS and other bootstrap approaches. Here, we present UFBoot2, which substantially accelerates UFBoot and reduces the risk of overestimating branch supports due to polytomies or severe model violations. Additionally, UFBoot2 provides suitable bootstrap resampling strategies for phylogenomic data. UFBoot2 is 778 times (median) faster than SBS and 8.4 times (median) faster than RAxML rapid bootstrap on tested data sets. UFBoot2 is implemented in the IQ-TREE software package version 1.6 and freely available at http://www.iqtree.org.}",
    issn = "0737-4038",
    doi = "10.1093/molbev/msx281",
    url = "https://doi.org/10.1093/molbev/msx281",
    eprint = "https://academic.oup.com/mbe/article-pdf/35/2/518/24367824/msx281.pdf"
}

@article{10.1093/bioinformatics/btab096,
    author = "Steenwyk, Jacob L and Buida, Thomas J, III and Labella, Abigail L and Li, Yuanning and Shen, Xing-Xing and Rokas, Antonis",
    title = "{PhyKIT: a broadly applicable UNIX shell toolkit for processing and analyzing phylogenomic data}",
    journal = "Bioinformatics",
    volume = "37",
    number = "16",
    pages = "2325-2331",
    year = "2021",
    month = "02",
    abstract = "{Diverse disciplines in biology process and analyze multiple sequence alignments (MSAs) and phylogenetic trees to evaluate their information content, infer evolutionary events and processes and predict gene function. However, automated processing of MSAs and trees remains a challenge due to the lack of a unified toolkit. To fill this gap, we introduce PhyKIT, a toolkit for the UNIX shell environment with 30 functions that process MSAs and trees, including but not limited to estimation of mutation rate, evaluation of sequence composition biases, calculation of the degree of violation of a molecular clock and collapsing bipartitions (internal branches) with low support.To demonstrate the utility of PhyKIT, we detail three use cases: (1) summarizing information content in MSAs and phylogenetic trees for diagnosing potential biases in sequence or tree data; (2) evaluating gene–gene covariation of evolutionary rates to identify functional relationships, including novel ones, among genes and (3) identify lack of resolution events or polytomies in phylogenetic trees, which are suggestive of rapid radiation events or lack of data. We anticipate PhyKIT will be useful for processing, examining and deriving biological meaning from increasingly large phylogenomic datasets.PhyKIT is freely available on GitHub (https://github.com/JLSteenwyk/PhyKIT), PyPi (https://pypi.org/project/phykit/) and the Anaconda Cloud (https://anaconda.org/JLSteenwyk/phykit) under the MIT license with extensive documentation and user tutorials (https://jlsteenwyk.com/PhyKIT).Supplementary data are available at Bioinformatics online.}",
    issn = "1367-4803",
    doi = "10.1093/bioinformatics/btab096",
    url = "https://doi.org/10.1093/bioinformatics/btab096",
    eprint = "https://academic.oup.com/bioinformatics/article-pdf/37/16/2325/39948152/btab096.pdf"
}

@article{eaton_toytree_2020,
    author = "Eaton, Deren A. R.",
    title = "Toytree: {A} minimalist tree visualization and manipulation library for {Python}",
    volume = "11",
    doi = "10.1111/2041-210X.13313",
    journal = "Methods in Ecology and Evolution",
    year = "2020",
    pages = "187--191"
}

@article{10.1093/molbev/msaa015,
    author = "Minh, Bui Quang and Schmidt, Heiko A and Chernomor, Olga and Schrempf, Dominik and Woodhams, Michael D and von Haeseler, Arndt and Lanfear, Robert",
    title = "{IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era}",
    journal = "Molecular Biology and Evolution",
    volume = "37",
    number = "5",
    pages = "1530-1534",
    year = "2020",
    month = "02",
    abstract = "{IQ-TREE (http://www.iqtree.org, last accessed February 6, 2020) is a user-friendly and widely used software package for phylogenetic inference using maximum likelihood. Since the release of version 1 in 2014, we have continuously expanded IQ-TREE to integrate a plethora of new models of sequence evolution and efficient computational approaches of phylogenetic inference to deal with genomic data. Here, we describe notable features of IQ-TREE version 2 and highlight the key advantages over other software.}",
    issn = "0737-4038",
    doi = "10.1093/molbev/msaa015",
    url = "https://doi.org/10.1093/molbev/msaa015",
    eprint = "https://academic.oup.com/mbe/article-pdf/37/5/1530/33386032/msaa015.pdf"
}

@article{Kalyaanamoorthy2017,
    author = "Kalyaanamoorthy, Subha and Minh, Bui Quang and Wong, Thomas K F and von Haeseler, Arndt and Jermiin, Lars S",
    type = "Journal Article",
    title = "ModelFinder: fast model selection for accurate phylogenetic estimates",
    journal = "Nature Methods",
    number = "6",
    doi = "10.1038/nmeth.4285",
    volume = "14",
    pages = "587--589",
    url = "https://doi.org/10.1038/nmeth.4285",
    year = "2017",
    abstract = "ModelFinder is a fast model-selection method that greatly improves the accuracy of phylogenetic estimates.",
    issn = "1548-7105",
    DA = "2017/06/01"
}

@article{Zhang2018,
    author = "Zhang, Chao and Rabiee, Maryam and Sayyari, Erfan and Mirarab, Siavash",
    type = "Journal Article",
    title = "ASTRAL-III: polynomial time species tree reconstruction from partially resolved gene trees",
    journal = "BMC Bioinformatics",
    number = "6",
    doi = "10.1186/s12859-018-2129-y",
    volume = "19",
    pages = "153",
    url = "https://doi.org/10.1186/s12859-018-2129-y",
    year = "2018",
    abstract = "Evolutionary histories can be discordant across the genome, and such discordances need to be considered in reconstructing the species phylogeny. ASTRAL is one of the leading methods for inferring species trees from gene trees while accounting for gene tree discordance. ASTRAL uses dynamic programming to search for the tree that shares the maximum number of quartet topologies with input gene trees, restricting itself to a predefined set of bipartitions.",
    issn = "1471-2105",
    DA = "2018/05/08"
}